{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "神经网络分类.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPat0SkMtjiDScP8KX8jO1/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/-Logging-related-network/blob/master/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%86%E7%B1%BB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cqy3vXXOMI06",
        "colab_type": "text"
      },
      "source": [
        "### <font size=5px color=\"red\">✦ *Google Colab 突破90分钟自动断开:</font>\n",
        "<p><font size=3px > 每60分钟自动运行代码以刷新90分钟断开限制. 打开 developer-settings (在你的浏览器) 快速健 Ctrl+Shift+I 然后按console 输入以下代码 Enter. ( mac 按 Option+Command+I)</p><b>复制以下隐藏代码粉贴在浏览器console！！不要关闭浏览器以免失效</b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Z8cK8h2Avbv",
        "colab_type": "text"
      },
      "source": [
        "<code>function ClickConnect(){\n",
        "console.log(\"Working\"); \n",
        "document.querySelector(\"colab-connect-button\").click() \n",
        "}setInterval(ClickConnect,6000)</code>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtClEMAMLVHw",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1efa27ea-4a1f-4c3e-921b-42c666449fde"
      },
      "source": [
        "#@markdown <h3>← 输入了代码后运行以防止断开</h>\n",
        "\n",
        "\n",
        "import IPython\n",
        "from google.colab import output\n",
        "\n",
        "display(IPython.display.Javascript('''\n",
        " function ClickConnect(){\n",
        "   btn = document.querySelector(\"colab-connect-button\")\n",
        "   if (btn != null){\n",
        "     console.log(\"Click colab-connect-button\"); \n",
        "     btn.click() \n",
        "     }\n",
        "   \n",
        "   btn = document.getElementById('ok')\n",
        "   if (btn != null){\n",
        "     console.log(\"Click reconnect\"); \n",
        "     btn.click() \n",
        "     }\n",
        "  }\n",
        "  \n",
        "setInterval(ClickConnect,60000)\n",
        "'''))\n",
        "\n",
        "print(\"Done.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              " function ClickConnect(){\n",
              "   btn = document.querySelector(\"colab-connect-button\")\n",
              "   if (btn != null){\n",
              "     console.log(\"Click colab-connect-button\"); \n",
              "     btn.click() \n",
              "     }\n",
              "   \n",
              "   btn = document.getElementById('ok')\n",
              "   if (btn != null){\n",
              "     console.log(\"Click reconnect\"); \n",
              "     btn.click() \n",
              "     }\n",
              "  }\n",
              "  \n",
              "setInterval(ClickConnect,60000)\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yd3MQph-PpH2",
        "colab_type": "text"
      },
      "source": [
        "#引入数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fokPJgr0Pw8B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "e3f30ffa-20b4-49e8-c0cc-54099db1f967"
      },
      "source": [
        "! git clone https://github.com/sunyingjian/numpy-.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'numpy-'...\n",
            "remote: Enumerating objects: 5, done.\u001b[K\n",
            "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 94 (delta 0), reused 0 (delta 0), pack-reused 89\u001b[K\n",
            "Unpacking objects: 100% (94/94), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2t-kbnBP1Sb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "686b5729-870f-4756-dd94-9a557c5644f7"
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TrAKecRQBeX",
        "colab_type": "text"
      },
      "source": [
        "#数据处理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS4jV_1VP8vw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "1e1dcf41-fda1-484c-fb03-1047f4dfe2e8"
      },
      "source": [
        "%matplotlib inline\n",
        "#%matplotlib inline 可以在Ipython编译器里直接使用，功能是可以内嵌绘图，并且可以省略掉plt.show()这一步。\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from pandas import set_option\n",
        "set_option(\"display.max_rows\", 10)#设置要显示的默认行数，显示的最大行数是10\n",
        "pd.options.mode.chained_assignment = None #为了在增加列表行数的时候防止出现setting with copy warning\n",
        "filename = 'facies_vectors.csv'\n",
        "training_data = pd.read_csv('/content/numpy-/3345train data.csv')\n",
        "training_data\n",
        "testing_data = pd.read_csv('/content/numpy-/3345test_data.csv')\n",
        "testing_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC</th>\n",
              "      <th>CAL</th>\n",
              "      <th>GR</th>\n",
              "      <th>K</th>\n",
              "      <th>RD</th>\n",
              "      <th>SP</th>\n",
              "      <th>Core Lithology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.038756</td>\n",
              "      <td>0.026970</td>\n",
              "      <td>0.094776</td>\n",
              "      <td>0.112543</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>0.614504</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.347382</td>\n",
              "      <td>0.090194</td>\n",
              "      <td>0.145150</td>\n",
              "      <td>0.202335</td>\n",
              "      <td>0.002744</td>\n",
              "      <td>0.339568</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.218400</td>\n",
              "      <td>0.059510</td>\n",
              "      <td>0.252165</td>\n",
              "      <td>0.711295</td>\n",
              "      <td>0.003874</td>\n",
              "      <td>0.918567</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.366064</td>\n",
              "      <td>0.082147</td>\n",
              "      <td>0.149304</td>\n",
              "      <td>0.201731</td>\n",
              "      <td>0.003290</td>\n",
              "      <td>0.343180</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.291877</td>\n",
              "      <td>0.079981</td>\n",
              "      <td>0.141782</td>\n",
              "      <td>0.289309</td>\n",
              "      <td>0.003455</td>\n",
              "      <td>0.344605</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>0.016947</td>\n",
              "      <td>0.138827</td>\n",
              "      <td>0.118400</td>\n",
              "      <td>0.358567</td>\n",
              "      <td>0.281687</td>\n",
              "      <td>0.302936</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>0.131043</td>\n",
              "      <td>0.069016</td>\n",
              "      <td>0.029354</td>\n",
              "      <td>0.060197</td>\n",
              "      <td>0.282093</td>\n",
              "      <td>0.253809</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>0.036182</td>\n",
              "      <td>0.032010</td>\n",
              "      <td>0.030090</td>\n",
              "      <td>0.071069</td>\n",
              "      <td>0.359737</td>\n",
              "      <td>0.435486</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364</th>\n",
              "      <td>0.014562</td>\n",
              "      <td>0.127332</td>\n",
              "      <td>0.035014</td>\n",
              "      <td>0.178579</td>\n",
              "      <td>0.803566</td>\n",
              "      <td>0.311641</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365</th>\n",
              "      <td>0.014427</td>\n",
              "      <td>0.134185</td>\n",
              "      <td>0.033460</td>\n",
              "      <td>0.186430</td>\n",
              "      <td>0.891134</td>\n",
              "      <td>0.311652</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>366 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           AC       CAL        GR         K        RD        SP  Core Lithology\n",
              "0    0.038756  0.026970  0.094776  0.112543  0.001420  0.614504               5\n",
              "1    0.347382  0.090194  0.145150  0.202335  0.002744  0.339568               7\n",
              "2    0.218400  0.059510  0.252165  0.711295  0.003874  0.918567               3\n",
              "3    0.366064  0.082147  0.149304  0.201731  0.003290  0.343180               7\n",
              "4    0.291877  0.079981  0.141782  0.289309  0.003455  0.344605               7\n",
              "..        ...       ...       ...       ...       ...       ...             ...\n",
              "361  0.016947  0.138827  0.118400  0.358567  0.281687  0.302936               6\n",
              "362  0.131043  0.069016  0.029354  0.060197  0.282093  0.253809               3\n",
              "363  0.036182  0.032010  0.030090  0.071069  0.359737  0.435486               5\n",
              "364  0.014562  0.127332  0.035014  0.178579  0.803566  0.311641               6\n",
              "365  0.014427  0.134185  0.033460  0.186430  0.891134  0.311652               6\n",
              "\n",
              "[366 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ueJhkUKQAAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "cc61350a-4a0a-4c84-b1f6-01246fe69b7f"
      },
      "source": [
        "# 1=粗砂岩  2=中砂岩   3=细砂岩\n",
        "# 4=粉砂岩 5=白云岩 6=石灰岩 7=泥岩\n",
        "\n",
        "facies_labels = ['CS', 'MS', 'FS', 'SS', 'DM',\n",
        "                 'LS', 'MDS']\n",
        "training_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC</th>\n",
              "      <th>CAL</th>\n",
              "      <th>GR</th>\n",
              "      <th>K</th>\n",
              "      <th>RD</th>\n",
              "      <th>SP</th>\n",
              "      <th>Core Lithology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.345000e+03</td>\n",
              "      <td>3.345000e+03</td>\n",
              "      <td>3.345000e+03</td>\n",
              "      <td>3.345000e+03</td>\n",
              "      <td>3.345000e+03</td>\n",
              "      <td>3345.000000</td>\n",
              "      <td>3345.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.107992e-01</td>\n",
              "      <td>1.450162e-01</td>\n",
              "      <td>1.662901e-01</td>\n",
              "      <td>3.464980e-01</td>\n",
              "      <td>4.917120e-02</td>\n",
              "      <td>0.418110</td>\n",
              "      <td>4.221525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.099814e-01</td>\n",
              "      <td>1.353280e-01</td>\n",
              "      <td>1.036918e-01</td>\n",
              "      <td>1.808527e-01</td>\n",
              "      <td>8.719010e-02</td>\n",
              "      <td>0.196548</td>\n",
              "      <td>2.079667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>1.000000e-08</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>1.000000e-07</td>\n",
              "      <td>1.000000e-09</td>\n",
              "      <td>-0.062739</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.594008e-01</td>\n",
              "      <td>5.225926e-02</td>\n",
              "      <td>8.778840e-02</td>\n",
              "      <td>2.079726e-01</td>\n",
              "      <td>1.270254e-02</td>\n",
              "      <td>0.325241</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.124234e-01</td>\n",
              "      <td>9.779821e-02</td>\n",
              "      <td>1.520483e-01</td>\n",
              "      <td>3.352124e-01</td>\n",
              "      <td>2.403313e-02</td>\n",
              "      <td>0.384527</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.574892e-01</td>\n",
              "      <td>1.850738e-01</td>\n",
              "      <td>2.359244e-01</td>\n",
              "      <td>4.811758e-01</td>\n",
              "      <td>4.815465e-02</td>\n",
              "      <td>0.439047</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 AC           CAL  ...           SP  Core Lithology\n",
              "count  3.345000e+03  3.345000e+03  ...  3345.000000     3345.000000\n",
              "mean   2.107992e-01  1.450162e-01  ...     0.418110        4.221525\n",
              "std    1.099814e-01  1.353280e-01  ...     0.196548        2.079667\n",
              "min    1.000000e-07  1.000000e-08  ...    -0.062739        1.000000\n",
              "25%    1.594008e-01  5.225926e-02  ...     0.325241        2.000000\n",
              "50%    2.124234e-01  9.779821e-02  ...     0.384527        4.000000\n",
              "75%    2.574892e-01  1.850738e-01  ...     0.439047        7.000000\n",
              "max    1.000000e+00  1.000000e+00  ...     1.000000        7.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vO1kUfC9QDol",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "bb3c5de6-41e7-4796-9368-f0b28cc9818a"
      },
      "source": [
        "# 1=粗砂岩  2=中砂岩   3=细砂岩\n",
        "# 4=粉砂岩 5=白云岩 6=石灰岩 7=泥岩\n",
        "\n",
        "facies_labels = ['CS', 'MS', 'FS', 'SS', 'DM',\n",
        "                 'LS', 'MDS']\n",
        "testing_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC</th>\n",
              "      <th>CAL</th>\n",
              "      <th>GR</th>\n",
              "      <th>K</th>\n",
              "      <th>RD</th>\n",
              "      <th>SP</th>\n",
              "      <th>Core Lithology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.214074</td>\n",
              "      <td>0.148747</td>\n",
              "      <td>0.147995</td>\n",
              "      <td>0.330942</td>\n",
              "      <td>0.046567</td>\n",
              "      <td>0.389129</td>\n",
              "      <td>4.215847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.108781</td>\n",
              "      <td>0.144160</td>\n",
              "      <td>0.087543</td>\n",
              "      <td>0.181384</td>\n",
              "      <td>0.080542</td>\n",
              "      <td>0.196271</td>\n",
              "      <td>2.072619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.003611</td>\n",
              "      <td>0.007118</td>\n",
              "      <td>0.005029</td>\n",
              "      <td>0.025971</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>-0.061365</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.162638</td>\n",
              "      <td>0.052989</td>\n",
              "      <td>0.079442</td>\n",
              "      <td>0.194786</td>\n",
              "      <td>0.012828</td>\n",
              "      <td>0.317109</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.215371</td>\n",
              "      <td>0.098682</td>\n",
              "      <td>0.132807</td>\n",
              "      <td>0.302396</td>\n",
              "      <td>0.022275</td>\n",
              "      <td>0.379000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.269240</td>\n",
              "      <td>0.179492</td>\n",
              "      <td>0.217013</td>\n",
              "      <td>0.456563</td>\n",
              "      <td>0.047410</td>\n",
              "      <td>0.435390</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.713160</td>\n",
              "      <td>0.994694</td>\n",
              "      <td>0.483319</td>\n",
              "      <td>0.994363</td>\n",
              "      <td>0.891134</td>\n",
              "      <td>0.997070</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               AC         CAL  ...          SP  Core Lithology\n",
              "count  366.000000  366.000000  ...  366.000000      366.000000\n",
              "mean     0.214074    0.148747  ...    0.389129        4.215847\n",
              "std      0.108781    0.144160  ...    0.196271        2.072619\n",
              "min      0.003611    0.007118  ...   -0.061365        1.000000\n",
              "25%      0.162638    0.052989  ...    0.317109        2.000000\n",
              "50%      0.215371    0.098682  ...    0.379000        4.000000\n",
              "75%      0.269240    0.179492  ...    0.435390        7.000000\n",
              "max      0.713160    0.994694  ...    0.997070        7.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2_eUnyJQHlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "d128fe2e-ce20-4fb7-f47b-f5db0f2b4aa2"
      },
      "source": [
        "correct_facies_labels = training_data['Core Lithology'].values\n",
        "correct_facies_labels_test = testing_data['Core Lithology'].values\n",
        "feature_vectors = training_data.drop(['Core Lithology'], axis=1)\n",
        "feature_vectors_test = testing_data.drop(['Core Lithology'], axis=1)\n",
        "feature_vectors.describe()\n",
        "feature_vectors_test.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC</th>\n",
              "      <th>CAL</th>\n",
              "      <th>GR</th>\n",
              "      <th>K</th>\n",
              "      <th>RD</th>\n",
              "      <th>SP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>366.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.214074</td>\n",
              "      <td>0.148747</td>\n",
              "      <td>0.147995</td>\n",
              "      <td>0.330942</td>\n",
              "      <td>0.046567</td>\n",
              "      <td>0.389129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.108781</td>\n",
              "      <td>0.144160</td>\n",
              "      <td>0.087543</td>\n",
              "      <td>0.181384</td>\n",
              "      <td>0.080542</td>\n",
              "      <td>0.196271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.003611</td>\n",
              "      <td>0.007118</td>\n",
              "      <td>0.005029</td>\n",
              "      <td>0.025971</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>-0.061365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.162638</td>\n",
              "      <td>0.052989</td>\n",
              "      <td>0.079442</td>\n",
              "      <td>0.194786</td>\n",
              "      <td>0.012828</td>\n",
              "      <td>0.317109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.215371</td>\n",
              "      <td>0.098682</td>\n",
              "      <td>0.132807</td>\n",
              "      <td>0.302396</td>\n",
              "      <td>0.022275</td>\n",
              "      <td>0.379000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.269240</td>\n",
              "      <td>0.179492</td>\n",
              "      <td>0.217013</td>\n",
              "      <td>0.456563</td>\n",
              "      <td>0.047410</td>\n",
              "      <td>0.435390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.713160</td>\n",
              "      <td>0.994694</td>\n",
              "      <td>0.483319</td>\n",
              "      <td>0.994363</td>\n",
              "      <td>0.891134</td>\n",
              "      <td>0.997070</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               AC         CAL          GR           K          RD          SP\n",
              "count  366.000000  366.000000  366.000000  366.000000  366.000000  366.000000\n",
              "mean     0.214074    0.148747    0.147995    0.330942    0.046567    0.389129\n",
              "std      0.108781    0.144160    0.087543    0.181384    0.080542    0.196271\n",
              "min      0.003611    0.007118    0.005029    0.025971    0.001420   -0.061365\n",
              "25%      0.162638    0.052989    0.079442    0.194786    0.012828    0.317109\n",
              "50%      0.215371    0.098682    0.132807    0.302396    0.022275    0.379000\n",
              "75%      0.269240    0.179492    0.217013    0.456563    0.047410    0.435390\n",
              "max      0.713160    0.994694    0.483319    0.994363    0.891134    0.997070"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYf1ygcjQNav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = feature_vectors \n",
        "X_test = feature_vectors_test\n",
        "y_train = correct_facies_labels\n",
        "y_test = correct_facies_labels_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdKCZtQXQj_s",
        "colab_type": "text"
      },
      "source": [
        "#RNN分类"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dMkg7aOALuv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "97c05aa7-c56a-4bfd-ea2e-d9a65f2ce4c0"
      },
      "source": [
        "import tensorflow as tf\n",
        "print('tensorflow:',format(tf.__version__))\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensorflow: 2.2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yUc0K9qIUfE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "d5f1be6f-00ae-4f07-82e1-c63cd4454854"
      },
      "source": [
        "model =  tf.keras.Sequential([\n",
        "                              tf.keras.layers.Embedding(input_dim=6,output_dim=64),\n",
        "                              tf.keras.layers.GRU(64,return_sequences=True),\n",
        "                              tf.keras.layers.SimpleRNN(32),\n",
        "                              tf.keras.layers.Dense(7)\n",
        "])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_21\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_12 (Embedding)     (None, None, 64)          384       \n",
            "_________________________________________________________________\n",
            "gru_6 (GRU)                  (None, None, 64)          24960     \n",
            "_________________________________________________________________\n",
            "simple_rnn_3 (SimpleRNN)     (None, 32)                3104      \n",
            "_________________________________________________________________\n",
            "dense_39 (Dense)             (None, 7)                 231       \n",
            "=================================================================\n",
            "Total params: 28,679\n",
            "Trainable params: 28,679\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWjhpr74J8ED",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='mse',\n",
        "              metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erY91C_TKL5l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ccb78d3b-6e4d-4b45-f37b-81256c1fba7a"
      },
      "source": [
        "history=model.fit(X_train,y_train,\n",
        "         epochs=100,\n",
        "         validation_data=(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "105/105 [==============================] - 2s 16ms/step - loss: 8.6977 - acc: 0.0580 - val_loss: 4.4830 - val_acc: 0.0546\n",
            "Epoch 2/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3629 - acc: 0.0676 - val_loss: 4.2856 - val_acc: 0.0355\n",
            "Epoch 3/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3262 - acc: 0.1181 - val_loss: 4.2849 - val_acc: 0.2186\n",
            "Epoch 4/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3267 - acc: 0.1070 - val_loss: 4.2842 - val_acc: 0.2186\n",
            "Epoch 5/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3261 - acc: 0.1280 - val_loss: 4.2847 - val_acc: 0.1448\n",
            "Epoch 6/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3274 - acc: 0.1013 - val_loss: 4.2857 - val_acc: 0.0355\n",
            "Epoch 7/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3293 - acc: 0.1082 - val_loss: 4.2858 - val_acc: 0.0355\n",
            "Epoch 8/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3294 - acc: 0.1070 - val_loss: 4.2859 - val_acc: 0.1448\n",
            "Epoch 9/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3263 - acc: 0.0945 - val_loss: 4.2899 - val_acc: 0.0546\n",
            "Epoch 10/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3329 - acc: 0.0906 - val_loss: 4.2893 - val_acc: 0.1448\n",
            "Epoch 11/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3381 - acc: 0.0951 - val_loss: 4.2866 - val_acc: 0.2186\n",
            "Epoch 12/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3261 - acc: 0.1118 - val_loss: 4.2843 - val_acc: 0.2186\n",
            "Epoch 13/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3366 - acc: 0.1247 - val_loss: 4.2876 - val_acc: 0.0546\n",
            "Epoch 14/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3277 - acc: 0.1106 - val_loss: 4.2841 - val_acc: 0.0546\n",
            "Epoch 15/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3290 - acc: 0.1297 - val_loss: 4.2843 - val_acc: 0.2186\n",
            "Epoch 16/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3268 - acc: 0.1130 - val_loss: 4.2921 - val_acc: 0.2186\n",
            "Epoch 17/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3306 - acc: 0.1103 - val_loss: 4.2861 - val_acc: 0.2186\n",
            "Epoch 18/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3250 - acc: 0.1052 - val_loss: 4.2844 - val_acc: 0.0546\n",
            "Epoch 19/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3302 - acc: 0.1480 - val_loss: 4.2843 - val_acc: 0.0355\n",
            "Epoch 20/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3363 - acc: 0.1157 - val_loss: 4.2862 - val_acc: 0.0546\n",
            "Epoch 21/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3278 - acc: 0.1148 - val_loss: 4.2850 - val_acc: 0.1448\n",
            "Epoch 22/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3266 - acc: 0.1342 - val_loss: 4.2862 - val_acc: 0.1448\n",
            "Epoch 23/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3251 - acc: 0.1555 - val_loss: 4.3021 - val_acc: 0.2186\n",
            "Epoch 24/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3261 - acc: 0.0915 - val_loss: 4.2840 - val_acc: 0.0546\n",
            "Epoch 25/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3257 - acc: 0.0930 - val_loss: 4.2859 - val_acc: 0.0546\n",
            "Epoch 26/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3285 - acc: 0.1133 - val_loss: 4.2841 - val_acc: 0.1448\n",
            "Epoch 27/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3264 - acc: 0.1193 - val_loss: 4.2872 - val_acc: 0.0355\n",
            "Epoch 28/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3269 - acc: 0.1025 - val_loss: 4.2842 - val_acc: 0.1448\n",
            "Epoch 29/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3285 - acc: 0.1336 - val_loss: 4.2884 - val_acc: 0.1448\n",
            "Epoch 30/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3216 - acc: 0.1205 - val_loss: 4.2873 - val_acc: 0.2186\n",
            "Epoch 31/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3250 - acc: 0.1540 - val_loss: 4.2903 - val_acc: 0.0546\n",
            "Epoch 32/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3304 - acc: 0.1220 - val_loss: 4.2850 - val_acc: 0.1448\n",
            "Epoch 33/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3267 - acc: 0.1052 - val_loss: 4.2995 - val_acc: 0.0546\n",
            "Epoch 34/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3320 - acc: 0.1369 - val_loss: 4.2860 - val_acc: 0.2186\n",
            "Epoch 35/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3223 - acc: 0.1223 - val_loss: 4.2920 - val_acc: 0.2186\n",
            "Epoch 36/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3268 - acc: 0.1106 - val_loss: 4.2934 - val_acc: 0.0546\n",
            "Epoch 37/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3286 - acc: 0.1277 - val_loss: 4.2848 - val_acc: 0.0546\n",
            "Epoch 38/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3305 - acc: 0.1378 - val_loss: 4.2947 - val_acc: 0.2186\n",
            "Epoch 39/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3284 - acc: 0.1187 - val_loss: 4.2955 - val_acc: 0.0546\n",
            "Epoch 40/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3208 - acc: 0.1229 - val_loss: 4.2844 - val_acc: 0.0546\n",
            "Epoch 41/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3290 - acc: 0.1034 - val_loss: 4.2941 - val_acc: 0.2186\n",
            "Epoch 42/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3290 - acc: 0.1417 - val_loss: 4.2876 - val_acc: 0.0355\n",
            "Epoch 43/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3263 - acc: 0.1199 - val_loss: 4.2911 - val_acc: 0.0546\n",
            "Epoch 44/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3294 - acc: 0.1262 - val_loss: 4.2859 - val_acc: 0.0546\n",
            "Epoch 45/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3289 - acc: 0.1175 - val_loss: 4.2872 - val_acc: 0.0546\n",
            "Epoch 46/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3253 - acc: 0.1333 - val_loss: 4.2844 - val_acc: 0.0546\n",
            "Epoch 47/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3248 - acc: 0.1414 - val_loss: 4.2847 - val_acc: 0.1448\n",
            "Epoch 48/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3244 - acc: 0.1575 - val_loss: 4.2877 - val_acc: 0.0546\n",
            "Epoch 49/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3272 - acc: 0.1133 - val_loss: 4.2887 - val_acc: 0.2186\n",
            "Epoch 50/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3303 - acc: 0.1339 - val_loss: 4.2909 - val_acc: 0.0546\n",
            "Epoch 51/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3194 - acc: 0.1342 - val_loss: 4.3254 - val_acc: 0.2186\n",
            "Epoch 52/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3379 - acc: 0.1399 - val_loss: 4.2841 - val_acc: 0.0546\n",
            "Epoch 53/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3332 - acc: 0.1151 - val_loss: 4.2874 - val_acc: 0.1448\n",
            "Epoch 54/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3251 - acc: 0.1324 - val_loss: 4.2862 - val_acc: 0.2186\n",
            "Epoch 55/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3277 - acc: 0.0894 - val_loss: 4.2862 - val_acc: 0.0355\n",
            "Epoch 56/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3314 - acc: 0.1659 - val_loss: 4.2842 - val_acc: 0.2186\n",
            "Epoch 57/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3324 - acc: 0.1635 - val_loss: 4.2842 - val_acc: 0.2186\n",
            "Epoch 58/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3256 - acc: 0.1608 - val_loss: 4.2923 - val_acc: 0.2186\n",
            "Epoch 59/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3265 - acc: 0.1752 - val_loss: 4.2846 - val_acc: 0.1448\n",
            "Epoch 60/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3228 - acc: 0.1575 - val_loss: 4.2851 - val_acc: 0.1448\n",
            "Epoch 61/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3244 - acc: 0.1525 - val_loss: 4.2840 - val_acc: 0.2186\n",
            "Epoch 62/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3242 - acc: 0.1504 - val_loss: 4.2909 - val_acc: 0.1448\n",
            "Epoch 63/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3308 - acc: 0.1555 - val_loss: 4.2863 - val_acc: 0.2186\n",
            "Epoch 64/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3254 - acc: 0.1372 - val_loss: 4.2843 - val_acc: 0.1448\n",
            "Epoch 65/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3239 - acc: 0.1614 - val_loss: 4.2862 - val_acc: 0.2186\n",
            "Epoch 66/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3251 - acc: 0.1462 - val_loss: 4.2845 - val_acc: 0.0546\n",
            "Epoch 67/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3235 - acc: 0.1199 - val_loss: 4.2847 - val_acc: 0.0546\n",
            "Epoch 68/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3252 - acc: 0.1572 - val_loss: 4.2852 - val_acc: 0.0355\n",
            "Epoch 69/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3278 - acc: 0.1587 - val_loss: 4.2918 - val_acc: 0.2186\n",
            "Epoch 70/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3323 - acc: 0.1623 - val_loss: 4.3007 - val_acc: 0.0546\n",
            "Epoch 71/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3244 - acc: 0.1474 - val_loss: 4.3115 - val_acc: 0.1448\n",
            "Epoch 72/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3266 - acc: 0.1522 - val_loss: 4.2842 - val_acc: 0.2186\n",
            "Epoch 73/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3279 - acc: 0.1429 - val_loss: 4.2841 - val_acc: 0.0546\n",
            "Epoch 74/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3228 - acc: 0.1259 - val_loss: 4.2845 - val_acc: 0.0546\n",
            "Epoch 75/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3222 - acc: 0.1357 - val_loss: 4.2927 - val_acc: 0.0546\n",
            "Epoch 76/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3279 - acc: 0.1363 - val_loss: 4.2930 - val_acc: 0.2186\n",
            "Epoch 77/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3360 - acc: 0.1465 - val_loss: 4.2872 - val_acc: 0.2186\n",
            "Epoch 78/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3193 - acc: 0.1438 - val_loss: 4.2842 - val_acc: 0.0546\n",
            "Epoch 79/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3226 - acc: 0.1339 - val_loss: 4.2879 - val_acc: 0.2186\n",
            "Epoch 80/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3287 - acc: 0.1393 - val_loss: 4.2888 - val_acc: 0.2186\n",
            "Epoch 81/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3217 - acc: 0.1390 - val_loss: 4.2885 - val_acc: 0.0546\n",
            "Epoch 82/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3319 - acc: 0.1259 - val_loss: 4.2853 - val_acc: 0.2186\n",
            "Epoch 83/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3250 - acc: 0.1312 - val_loss: 4.2866 - val_acc: 0.0546\n",
            "Epoch 84/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3248 - acc: 0.1274 - val_loss: 4.2843 - val_acc: 0.0546\n",
            "Epoch 85/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3259 - acc: 0.1309 - val_loss: 4.2848 - val_acc: 0.2186\n",
            "Epoch 86/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3199 - acc: 0.1333 - val_loss: 4.3002 - val_acc: 0.0546\n",
            "Epoch 87/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3237 - acc: 0.1208 - val_loss: 4.2847 - val_acc: 0.0546\n",
            "Epoch 88/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3286 - acc: 0.1244 - val_loss: 4.2845 - val_acc: 0.2186\n",
            "Epoch 89/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3278 - acc: 0.1333 - val_loss: 4.2889 - val_acc: 0.2186\n",
            "Epoch 90/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3304 - acc: 0.1345 - val_loss: 4.2863 - val_acc: 0.0546\n",
            "Epoch 91/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3231 - acc: 0.1286 - val_loss: 4.2841 - val_acc: 0.0546\n",
            "Epoch 92/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3263 - acc: 0.1384 - val_loss: 4.2920 - val_acc: 0.0546\n",
            "Epoch 93/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3264 - acc: 0.1303 - val_loss: 4.2869 - val_acc: 0.0546\n",
            "Epoch 94/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3266 - acc: 0.1271 - val_loss: 4.2840 - val_acc: 0.2186\n",
            "Epoch 95/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3264 - acc: 0.1408 - val_loss: 4.2867 - val_acc: 0.2186\n",
            "Epoch 96/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3249 - acc: 0.1345 - val_loss: 4.2885 - val_acc: 0.2186\n",
            "Epoch 97/100\n",
            "105/105 [==============================] - 1s 10ms/step - loss: 4.3198 - acc: 0.1471 - val_loss: 4.2951 - val_acc: 0.2186\n",
            "Epoch 98/100\n",
            "105/105 [==============================] - 1s 10ms/step - loss: 4.3272 - acc: 0.1306 - val_loss: 4.2848 - val_acc: 0.2186\n",
            "Epoch 99/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3243 - acc: 0.1265 - val_loss: 4.2855 - val_acc: 0.2186\n",
            "Epoch 100/100\n",
            "105/105 [==============================] - 1s 9ms/step - loss: 4.3260 - acc: 0.1297 - val_loss: 4.2862 - val_acc: 0.0546\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJeqcSkoLhWd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ead8a734-92eb-44fc-de4a-e7c159938901"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Hr4OVpsMBjQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "8723bf0e-3331-48f2-e988-368d772e9083"
      },
      "source": [
        "plt.plot(history.epoch,history.history.get('acc'),label='acc')\n",
        "plt.plot(history.epoch,history.history.get('val_acc'),label='val_acc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f625073ce48>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rq2MjdxvMIDC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "224b3262-e0ad-44a1-fbb2-d02602c03d2e"
      },
      "source": [
        "plt.plot(history.epoch,history.history.get('loss'),label='loss')\n",
        "plt.plot(history.epoch,history.history.get('val_loss'),label='val_loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6250c43470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}